Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View
2020585 citationsDeli Chen, Yankai Lin et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Xu Sun's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xu Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xu Sun more than expected).
This network shows the impact of papers produced by Xu Sun. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xu Sun. The network helps show where Xu Sun may publish in the future.
Co-authorship network of co-authors of Xu Sun
This figure shows the co-authorship network connecting the top 25 collaborators of Xu Sun.
A scholar is included among the top collaborators of Xu Sun based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Xu Sun. Xu Sun is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Fenglin, Yuanxin Liu, Xuancheng Ren, Kai Lei, & Xu Sun. (2019). Aligning Visual Regions and Textual Concepts: Learning Fine-Grained Image Representations for Image Captioning.. arXiv (Cornell University).1 indexed citations
12.
Lin, Junyang, Xu Sun, Xuancheng Ren, et al.. (2018). Deconvolution-Based Global Decoding for Neural Machine Translation. arXiv (Cornell University). 3260–3271.3 indexed citations
13.
Ren, Xuancheng, et al.. (2018). Building an Ellipsis-aware Chinese Dependency Treebank for Web Text. Language Resources and Evaluation.1 indexed citations
Huang, Shen, Xu Sun, & Houfeng Wang. (2017). Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure. International Joint Conference on Natural Language Processing. 1. 184–193.16 indexed citations
17.
Sun, Xu. (2014). Structure Regularization for Structured Prediction. Neural Information Processing Systems. 27. 2402–2410.27 indexed citations
Sun, Xu, et al.. (2010). Learning Phrase-Based Spelling Error Models from Clickthrough Data. Meeting of the Association for Computational Linguistics. 266–274.34 indexed citations
20.
Gao, Jianfeng, et al.. (2010). A Large Scale Ranker-Based System for Search Query Spelling Correction. International Conference on Computational Linguistics. 358–366.74 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.